With more than 8 million new cases of active disease and nearly 1.5 million deaths annually, TB is a global health emergency of overwhelming proportions. Treatment of TB faces several challenges including the complex and lengthy treatment required, the range of physiological conditions of Mycobacterium tuberculosis (MTB) in host macrophages that contributes to the need for prolonged treatment, and the alarming emergence of drug resistance that is amplified by the difficulties of ensuring compliance with long and toxic drug regimens. There is thus an urgent and recognized need for new TB drugs and drug combinations that will (1) synergize with existing drugs to shorten the duration of TB treatment, (2) inhibit new targets and target combinations so that they can be used for MDR and XDR TB, (3) work against TB in a range of physiological states in the host, and (4) potentially target host genes to enable host-directed adjuvant therapies. There is also a pressing need to comprehensively identify the mechanisms of acquired drug resistance in MTB. The goal of this proposal is to identify and validate novel targets for drugs and drug synergies for TB. We will computationally predict targets using established algorithms with in silico models of the MTB and macrophage regulatory and metabolic networks, and we will experimentally validate predicted targets in MTB-infected macrophages. The first specific aim is to identify and validate mechanisms of intrinsic and acquired drug resistance. We have developed a novel strategy to do so. Inhibition of intrinsic drug resistance has been shown to potentiate existing drug treatments. These mechanisms provide novel targets for drug synergies that are not typically identified by existing drug screens. Intrinsic resistance mechanisms are also potential targets for acquired drug resistance mutations. The second specific aim is to identify and validate novel metabolic targets for drugs and drug synergies. We will use our validated metabolic modeling algorithms to implement novel strategies for predicting targets and target combinations with specific relevance for host infection, and for predicting metabolic targets that potentiate existin drugs. The third specific aim is to identify and validate essential MTB-macrophage metabolic interactions to identify targets for drug synergies. We will apply our modeling algorithms with a metabolic model of the MTB-macrophage system. We will implement a novel strategy for identify pairs of bacterial and host genes whose disruption synergizes to inhibit MTB survival during infection.